README.md

metadata

language:
  - or
license: apache-2.0
base_model: openai/whisper-large-v2
tags:
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_13_0
metrics:
  - wer
model-index:
  - name: whisper-large-odiya
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 13
          type: mozilla-foundation/common_voice_13_0
          config: or
          split: test
          args: or
        metrics:
          - name: Wer
            type: wer
            value: 18.45270639693822

whisper-large-odiya

This model is a fine-tuned version of openai/whisper-large-v2 on the Common Voice 13 dataset. It achieves the following results on the evaluation set:

• Loss: 0.2808
• Wer Ortho: 45.8771
• Wer: 18.4527

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 1e-05
• train_batch_size: 8
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 20
• training_steps: 1000

Training results

Training Loss	Epoch	Step	Validation Loss	Wer Ortho	Wer
0.0019	9.71	500	0.2362	45.4898	19.3002
0.0001	19.42	1000	0.2808	45.8771	18.4527

Framework versions

• Transformers 4.31.0
• Pytorch 2.0.1+cu118
• Datasets 2.13.1
• Tokenizers 0.13.3